期刊
EUROPEAN PHYSICAL JOURNAL C
卷 77, 期 10, 页码 -出版社
SPRINGER
DOI: 10.1140/epjc/s10052-017-5224-8
关键词
-
资金
- German Research Foundation (DFG) through the Forschergruppe New Physics at the Large Hadron Collider [FOR 2239]
- Helmholtz Alliance Physics at the Terascale
- BMBF-FSP 101
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据